UGN: U-shape network based on graph convolution for 3D point cloud semantic segmentation

2020 ◽  
Author(s):  
Shaojie Guan ◽  
Xingwei Li ◽  
Jiating Jin ◽  
Xinlong Li ◽  
Yizhi Ge
Keyword(s):  
Point Cloud ◽  
Semantic Segmentation ◽  
3D Point Cloud

scholarly journals SEMANTIC SEGMENTATION FOR BUILDING FAÇADE 3D POINT CLOUD FROM 2D ORTHOPHOTO IMAGES USING TRANSFER LEARNING

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 201-206
Author(s):  
A. Murtiyoso ◽  
C. Lhenry ◽  
T. Landes ◽  
P. Grussenmeyer ◽  
E. Alby
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Point Cloud ◽  
Semantic Segmentation ◽  
3D Point Cloud ◽  
Semantic Classification ◽  
3D Data ◽  
The Neural Network ◽  
Building Facades ◽  
Open Question

Abstract. The task of semantic segmentation is an important one in the context of 3D building modelling. Indeed, developments in 3D generation techniques have rendered the point cloud ubiquitous. However pure data acquisition only captures geometric information and semantic classification remains to be performed, often manually, in order to give a tangible sense to the 3D data. Recently progress in computing power also opened the way for massive application of deep learning methods, including for semantic segmentation purposes. Although well established in the processing of 2D images, deep learning solutions remain an open question for 3D data. In this study, we aim to benefit from the vastly more developed 2D semantic segmentation by performing transfer learning on a photogrammetric orthoimage. The neural network was trained using labelled and rectified images of building façades. Another programme was then written to permit the passage between 2D orthoimage and 3D point cloud. Results show that the approach worked well and presents an alternative to help the automation process for point cloud semantic segmentation, at least in the case of photogrammetric data.

scholarly journals Bottleneck Identification to Semantic Segmentation of Industrial 3D Point Cloud Scene via Deep Learning

2021 ◽  
Author(s):  
Romain Cazorla ◽  
Line Poinel ◽  
Panagiotis Papadakis ◽  
Cédric Buche
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Oil And Gas ◽  
Synthetic Data ◽  
Semantic Segmentation ◽  
Object Size ◽  
3D Point Cloud ◽  
Industrial Plants ◽  
Bottleneck Identification ◽  
Data Adaptive

Point cloud acquisition techniques are an essential tool for the digitization of industrial plants, yet the bulk of a designer's work remains manual. A first step to automatize drawing generation is to extract the semantics of the point cloud. Towards this goal, we investigate the use of deep learning to semantically segment oil and gas industrial scenes. We focus on domain characteristics such as high variation of object size, increased concavity and lack of annotated data, which hampers the use of conventional approaches. To address these issues, we advocate the use of synthetic data, adaptive downsampling and context sharing.

Towards 3D LiDAR-based semantic scene understanding of 3D point cloud sequences: The SemanticKITTI Dataset

2021 ◽  
pp. 027836492110067
Author(s):  
Jens Behley ◽  
Martin Garbade ◽  
Andres Milioto ◽  
Jan Quenzel ◽  
Sven Behnke ◽  
...  
Keyword(s):  
Point Cloud ◽  
Scene Understanding ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Lessons Learned ◽  
Multiple Point ◽  
3D Point Cloud ◽  
Core Capability ◽  
Semantic Scene ◽  
3D Lidar

A holistic semantic scene understanding exploiting all available sensor modalities is a core capability to master self-driving in complex everyday traffic. To this end, we present the SemanticKITTI dataset that provides point-wise semantic annotations of Velodyne HDL-64E point clouds of the KITTI Odometry Benchmark. Together with the data, we also published three benchmark tasks for semantic scene understanding covering different aspects of semantic scene understanding: (1) semantic segmentation for point-wise classification using single or multiple point clouds as input; (2) semantic scene completion for predictive reasoning on the semantics and occluded regions; and (3) panoptic segmentation combining point-wise classification and assigning individual instance identities to separate objects of the same class. In this article, we provide details on our dataset showing an unprecedented number of fully annotated point cloud sequences, more information on our labeling process to efficiently annotate such a vast amount of point clouds, and lessons learned in this process. The dataset and resources are available at http://www.semantic-kitti.org .

scholarly journals Fast 3D Semantic Mapping in Road Scenes

2019 ◽  
Vol 9 (4) ◽  
pp. 631 ◽  
Author(s):  
Xuanpeng Li ◽  
Dong Wang ◽  
Huanxuan Ao ◽  
Rachid Belaroussi ◽  
Dominique Gruyer
Keyword(s):  
3D Reconstruction ◽  
Autonomous Navigation ◽  
Point Cloud ◽  
Semantic Information ◽  
Computational Cost ◽  
Semantic Segmentation ◽  
Semantic Mapping ◽  
3D Point Cloud ◽  
Semantic Inference ◽  
Mapping System

Fast 3D reconstruction with semantic information in road scenes is of great requirements for autonomous navigation. It involves issues of geometry and appearance in the field of computer vision. In this work, we propose a fast 3D semantic mapping system based on the monocular vision by fusion of localization, mapping, and scene parsing. From visual sequences, it can estimate the camera pose, calculate the depth, predict the semantic segmentation, and finally realize the 3D semantic mapping. Our system consists of three modules: a parallel visual Simultaneous Localization And Mapping (SLAM) and semantic segmentation module, an incrementally semantic transfer from 2D image to 3D point cloud, and a global optimization based on Conditional Random Field (CRF). It is a heuristic approach that improves the accuracy of the 3D semantic labeling in light of the spatial consistency on each step of 3D reconstruction. In our framework, there is no need to make semantic inference on each frame of sequence, since the 3D point cloud data with semantic information is corresponding to sparse reference frames. It saves on the computational cost and allows our mapping system to perform online. We evaluate the system on road scenes, e.g., KITTI, and observe a significant speed-up in the inference stage by labeling on the 3D point cloud.

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